HDIofMCMC               Compute Highest-Density Interval
alt_delta               Rescorla-Wagner (Delta) Model
alt_gamma               Rescorla-Wagner (Gamma) Model
bandit2arm_delta        Rescorla-Wagner (Delta) Model
bandit4arm2_kalman_filter
                        Kalman Filter
bandit4arm_2par_lapse   3 Parameter Model, without C (choice
                        perseveration), R (reward sensitivity), and P
                        (punishment sensitivity). But with xi (noise)
bandit4arm_4par         4 Parameter Model, without C (choice
                        perseveration)
bandit4arm_lapse        5 Parameter Model, without C (choice
                        perseveration) but with xi (noise)
bandit4arm_lapse_decay
                        5 Parameter Model, without C (choice
                        perseveration) but with xi (noise). Added decay
                        rate (Niv et al., 2015, J. Neuro).
bandit4arm_singleA_lapse
                        4 Parameter Model, without C (choice
                        perseveration) but with xi (noise). Single
                        learning rate both for R and P.
banditNarm_2par_lapse   3 Parameter Model, without C (choice
                        perseveration), R (reward sensitivity), and P
                        (punishment sensitivity). But with xi (noise)
banditNarm_4par         4 Parameter Model, without C (choice
                        perseveration)
banditNarm_delta        Rescorla-Wagner (Delta) Model
banditNarm_kalman_filter
                        Kalman Filter
banditNarm_lapse        5 Parameter Model, without C (choice
                        perseveration) but with xi (noise)
banditNarm_lapse_decay
                        5 Parameter Model, without C (choice
                        perseveration) but with xi (noise). Added decay
                        rate (Niv et al., 2015, J. Neuro).
banditNarm_singleA_lapse
                        4 Parameter Model, without C (choice
                        perseveration) but with xi (noise). Single
                        learning rate both for R and P.
bart_ewmv               Exponential-Weight Mean-Variance Model
bart_par4               Re-parameterized version of BART model with 4
                        parameters
cgt_cm                  Cumulative Model
choiceRT_ddm            Drift Diffusion Model
choiceRT_ddm_single     Drift Diffusion Model
cra_exp                 Exponential Subjective Value Model
cra_linear              Linear Subjective Value Model
dbdm_prob_weight        Probability Weight Function
dd_cs                   Constant-Sensitivity (CS) Model
dd_cs_single            Constant-Sensitivity (CS) Model
dd_exp                  Exponential Model
dd_hyperbolic           Hyperbolic Model
dd_hyperbolic_single    Hyperbolic Model
estimate_mode           Function to estimate mode of MCMC samples
extract_ic              Extract Model Comparison Estimates
gng_m1                  RW + noise
gng_m2                  RW + noise + bias
gng_m3                  RW + noise + bias + pi
gng_m4                  RW (rew/pun) + noise + bias + pi
igt_orl                 Outcome-Representation Learning Model
igt_pvl_decay           Prospect Valence Learning (PVL) Decay-RI
igt_pvl_delta           Prospect Valence Learning (PVL) Delta
igt_vpp                 Value-Plus-Perseverance
multiplot               Function to plot multiple figures
peer_ocu                Other-Conferred Utility (OCU) Model
plotDist                Plots the histogram of MCMC samples.
plotHDI                 Plots highest density interval (HDI) from
                        (MCMC) samples and prints HDI in the R console.
                        HDI is indicated by a red line. Based on John
                        Kruschke's codes.
plotInd                 Plots individual posterior distributions, using
                        the stan_plot function of the rstan package
printFit                Print model-fits (mean LOOIC or WAIC values in
                        addition to Akaike weights) of hBayesDM Models
prl_ewa                 Experience-Weighted Attraction Model
prl_fictitious          Fictitious Update Model
prl_fictitious_multipleB
                        Fictitious Update Model
prl_fictitious_rp       Fictitious Update Model, with separate learning
                        rates for positive and negative prediction
                        error (PE)
prl_fictitious_rp_woa   Fictitious Update Model, with separate learning
                        rates for positive and negative prediction
                        error (PE), without alpha (indecision point)
prl_fictitious_woa      Fictitious Update Model, without alpha
                        (indecision point)
prl_rp                  Reward-Punishment Model
prl_rp_multipleB        Reward-Punishment Model
pstRT_ddm               Drift Diffusion Model
pstRT_rlddm1            Reinforcement Learning Drift Diffusion Model 1
pstRT_rlddm6            Reinforcement Learning Drift Diffusion Model 6
pst_Q                   Q Learning Model
pst_gainloss_Q          Gain-Loss Q Learning Model
ra_noLA                 Prospect Theory, without loss aversion (LA)
                        parameter
ra_noRA                 Prospect Theory, without risk aversion (RA)
                        parameter
ra_prospect             Prospect Theory
rdt_happiness           Happiness Computational Model
rhat                    Function for extracting Rhat values from an
                        hBayesDM object
task2AFC_sdt            Signal detection theory model
ts_par4                 Hybrid Model, with 4 parameters
ts_par6                 Hybrid Model, with 6 parameters
ts_par7                 Hybrid Model, with 7 parameters (original
                        model)
ug_bayes                Ideal Observer Model
ug_delta                Rescorla-Wagner (Delta) Model
wcs_sql                 Sequential Learning Model
